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Malmquist index measurement for sustainability enhancement in Chinese municipalities and provinces

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  • Sueyoshi, Toshiyuki
  • Goto, Mika
  • Wang, Derek

Abstract

This study discusses a use of DEA environmental assessment in a time horizon. We use a radial approach to examine a dynamic change of index measures related to China's regional development and industrial pollution prevention. In applying the proposed assessment to a time-series data, we need to examine components that produce a shift of an efficiency frontier in a time horizon. A unique feature of the proposed assessment is that it incorporates a framework of Malmquist index to examine the frontier shift among multiple periods. Here, the frontier shift indicates a progress potential on technology development and/or managerial improvement among observed periods. Thus, it is not an efficiency measure. Rather, the measurement eliminates inefficiency by shifting an organization to an efficiency frontier. In this study, the index is conceptually separated into 6 subcomponents, which are further disaggregated into 12 different elements (6 subcomponents×2 disposability concepts) under natural and managerial disposability concepts. The proposed index measurement needs to consider a possible occurrence of a frontier crossover among different periods because technology diffusion on production activities usually has a time lag until it becomes really effective. Methodologically, this study conveys the message that DEA is an approximation method, so being not perfect. Therefore, it is necessary for us to use several different approaches (e.g., potential improvement by measuring a frontier shift and efficiency analysis) to obtain reliable empirical evidence. As an application, this study examines the level of sustainability related to 30 municipalities and provinces in China from 2003 to 2014. The following six policy implications are obtained in this study. First, all municipalities and provinces have made a considerable progress on their economies and pollution prevention efforts. Second, the effect of the frontier crossover is limited in China because they attain a high level of their potentials on economic development and pollution prevention. Of course, this effort is within the Chinese boundary, not international, because this study does not compare them with other industrial nations. Third, China needs to allocate its resources to western provinces at a level that it has used for coastal developments. Fourth, the government should reinforce efforts on environmental protection in central and southern regions. Fifth, the government has focused on the sustainability development in urban municipalities and provinces and then has gradually shifted the policy influence toward rural areas. Finally, different empirical results are confirmed by the potential analysis which consists of the Malmquist index measurement and the efficiency analysis. Both approaches need to be examined for future planning on Chinese regional development.

Suggested Citation

  • Sueyoshi, Toshiyuki & Goto, Mika & Wang, Derek, 2017. "Malmquist index measurement for sustainability enhancement in Chinese municipalities and provinces," Energy Economics, Elsevier, vol. 67(C), pages 554-571.
    • Handle: RePEc:eee:eneeco:v:67:y:2017:i:c:p:554-571
    DOI: 10.1016/j.eneco.2017.08.026
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    Cited by:

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    2. Fang Chen & Tao Zhao & Di Wang, 2022. "Research on China Cities’ Total Factor Productivity of Carbon Emission: Based on Decoupling Effect," IJERPH, MDPI, vol. 19(4), pages 1-16, February.
    3. Wei, Yigang & Li, Yan & Wu, Meiyu & Li, Yingbo, 2019. "The decomposition of total-factor CO2 emission efficiency of 97 contracting countries in Paris Agreement," Energy Economics, Elsevier, vol. 78(C), pages 365-378.
    4. Jinchao Li & Yuwei Xiang & Huanyu Jia & Lin Chen, 2018. "Analysis of Total Factor Energy Efficiency and Its Influencing Factors on Key Energy-Intensive Industries in the Beijing-Tianjin-Hebei Region," Sustainability, MDPI, vol. 10(1), pages 1-17, January.
    5. Zhang, Yue-Jun & Jiang, Lin & Shi, Wei, 2020. "Exploring the growth-adjusted energy-emission efficiency of transportation industry in China," Energy Economics, Elsevier, vol. 90(C).
    6. Longwu Liang & Zhen Bo Wang & Dong Luo & Ying Wei & Jingwen Sun, 2020. "Synergy effects and it’s influencing factors of China’s high technological innovation and regional economy," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-25, May.
    7. Ruyin Long & Qin Zhang & Hong Chen & Meifen Wu & Qianwen Li, 2020. "Measurement of the Energy Intensity of Human Well-Being and Spatial Econometric Analysis of Its Influencing Factors," IJERPH, MDPI, vol. 17(1), pages 1-21, January.
    8. Shen, Neng & Peng, Hui & Wang, Qunwei, 2021. "Spatial dependence, agglomeration externalities and the convergence of carbon productivity," Socio-Economic Planning Sciences, Elsevier, vol. 78(C).
    9. Khoshroo, Alireza & Izadikhah, Mohammad & Emrouznejad, Ali, 2022. "Total factor energy productivity considering undesirable pollutant outputs: A new double frontier based malmquist productivity index," Energy, Elsevier, vol. 258(C).
    10. Xiaoyang Zhou & Hao Chen & Hao Wang & Benjamin Lev & Lifang Quan, 2019. "Natural and Managerial Disposability Based DEA Model for China’s Regional Environmental Efficiency Assessment," Energies, MDPI, vol. 12(18), pages 1-20, September.
    11. Shuangjie Li & Hongyu Diao & Liming Wang & Chunqi Li, 2021. "Energy Efficiency Measurement: A VO TFEE Approach and Its Application," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    12. Wenhao Qi & Changxing Song & Meng Sun & Liguo Wang & Youcheng Han, 2022. "Sustainable Growth Drivers: Unveiling the Role Played by Carbon Productivity," IJERPH, MDPI, vol. 19(3), pages 1-25, January.

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    Keywords

    Malmquist index; Sustainability; DEA environmental assessment; China;
    All these keywords.

    JEL classification:

    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • Q56 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environment and Development; Environment and Trade; Sustainability; Environmental Accounts and Accounting; Environmental Equity; Population Growth

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